Data-Driven Optimized Output Regulation for Markov Jump Linear Systems and Its Application

The linear optimized output regulation problem (LOORP) for Markov jump linear systems (MJLSs) is investigated in this article. First, by improving previous methods for solving the regulator equations, a model-based scheme is proposed to address the LOORP of MJLSs. Subsequently, a reinforcement learning (RL)-based iteration scheme is developed, which enables the solution of the LOORP even when the system dynamics are partially unknown and the initial control gains are unstable. Finally, an LCL-coupled inverter-based distributed generation system is provided to demonstrate the performance of the proposed RL-based scheme.